Part 1

Building Exposure

Part 2

Assuming each city in San Mateo is increasing vehicle count by the same amount each decade, we can use EMFAC data to determine how many vehicles there will be in our flood risk zone between 2020-2050. In 2020 there is expected to be 5645 cars, in 2030 there is expected to be 7997.906, in 2030 there is expected be 9487.590, and in 2050 there is expected to be 10106.098 vehicles. Households with no vehicles in our study area is also projected to increase by the same percentage and same with households with one vehicle. When determining flood risk for these vehicles, we need to remember that we are using household flood risk as our test, so it will look like there is little to no risk for those households with 1/0 vehicles. Obviously, this is not the case, however, this particular model is looking at vehicle-related flood damage and thus cannot capture accurately the damage incurred by households.

Part 3

Part 4

Vulnerability Data

Part 5

Risk Estimation

## # A tibble: 10 × 10
##      SLR `2020` `2030` `2040` `2050` `2060` `2070` `2080` `2090` `2100`
##    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
##  1     0  0.942  0.923  0.793  0.508  0.235  0.094  0.033  0.011  0.005
##  2    25  0      0.051  0.198  0.453  0.581  0.44   0.249  0.128  0.071
##  3    50  0      0      0.001  0.035  0.176  0.363  0.409  0.313  0.19 
##  4    75  0      0      0      0      0.007  0.099  0.224  0.296  0.29 
##  5   100  0      0      0      0      0      0.004  0.075  0.162  0.219
##  6   125  0      0      0      0      0      0      0.01   0.064  0.126
##  7   150  0      0      0      0      0      0      0      0.025  0.055
##  8   175  0      0      0      0      0      0      0      0.001  0.034
##  9   200  0      0      0      0      0      0      0      0      0.01 
## 10   500  0      0      0      0      0      0      0      0      0

Now we have projected flood risk between 2020 - 2050 and its associated $ damage. It seems like 2020 and 2030 are going to be the most costly years

## # A tibble: 3 × 2
##   GEOID            aal
## * <chr>          <dbl>
## 1 060816103021  45370.
## 2 060816103032 346568.
## 3 060816103034 490361.
## [1] "$882,298.1"

Here we can visually see the AAL across our block groups. The darkest chunk has the middle-most amount of buildings but the greatest loss, the orange has the most buildings and a little less loss, the white chunk has the least amount of buildings and the least amount of loss. This probably has to do with the fact that the white chunk is the most inland versus the darkest chunk which is closest to the water and the canals.Waterfront properties generally are more expensive than inland ones so it would makes sense that despite there being a fair amount of buildings, there is a huge loss compared to the middle chunk. Even at the block level, however, our results may not be granular enough to fully understand the placement of the buildings and their associated AALs. This would be an interesting starting point to look at cost of housing, housing tenancy, and AAL affects.

Monte Carlo Simulation 

## [1] "$882,394.5"